Attention Modules
Bottleneck Transformer Block
Introduced by Srinivas et al. in Bottleneck Transformers for Visual RecognitionA Bottleneck Transformer Block is a block used in Bottleneck Transformers that replaces the spatial 3 × 3 convolution layer in a Residual Block with Multi-Head Self-Attention (MHSA).
Source: Bottleneck Transformers for Visual Recognition
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| whole slide images | 2 | 11.11% |
| Instance Segmentation | 2 | 11.11% |
| Anomaly Detection In Surveillance Videos | 1 | 5.56% |
| Optical Flow Estimation | 1 | 5.56% |
| Classification | 1 | 5.56% |
| Emotion Recognition | 1 | 5.56% |
| Self-Supervised Learning | 1 | 5.56% |
| Graph Neural Network | 1 | 5.56% |
| Semantic Segmentation | 1 | 5.56% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
1x1 Convolution
|
Convolutions | |
Attention
|
Attention Mechanisms | |
Multi-Head Attention
|
Attention Modules | |
Pointwise Convolution
|
Convolutions | |
Residual Connection
|
Skip Connections |
